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Lecturer(s)
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Šembera Jan, doc. Ing. Ph.D.
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Course content
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Lectures: 1. Numerical methods of looking for extremals of functions, minimization of a function of one variable (Fibonacci method and the golden section method) and Newton Raphson method 2.-3. Minimization of a function of more variables without restrictions: Nelder-Mead method, gradient methods, conjugate gradient method. 4.-5. Minimization of a function of more variables with equality constraints: Lagrange multiplicator method 6.-7. Minimization of a function of more variables with inequality constraints (mathematical programming): formulation of the problem for Matlab, derivation of Kuhn-Tucker conditions. The tutorials are led in a computer room using the MATLAB software.
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Learning activities and teaching methods
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Monological explanation (lecture, presentation,briefing)
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Learning outcomes
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The course develops the students' knowledge in the field of basic methods of optimization problem solution. It connects the theoretical lecture with solution of specific practical problems using the MATLAB software. After the course, the student is able to choose a proper
After the course, the student is able to choose a proper method for solution of his optimization problem and propose the corresponding algorithm in the MATLAB SW.
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Prerequisites
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Unspecified
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Assessment methods and criteria
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Written exam
Requirements for getting a credit are activity at the seminars and successful passing the tests
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Recommended literature
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R.FLETCHER. Practical methods of optimization.. 1987. ISBN ISBN 0-471-91547-.
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